Bayesian anomaly detection methods for social networks
Abstract
Learning the network structure of a large graph is computationally demanding, and dynamically monitoring the network over time for any changes in structure threatens to be more challenging still. This paper presents a two-stage method for anomaly detection in dynamic graphs: the first stage uses simple, conjugate Bayesian models for discrete time counting processes to track the pairwise links of all nodes in the graph to assess normality of behavior; the second stage applies standard network inference tools on a greatly reduced subset of potentially anomalous nodes. The utility of the method is demonstrated on simulated and real data sets.
Cite
@article{arxiv.1011.1788,
title = {Bayesian anomaly detection methods for social networks},
author = {Nicholas A. Heard and David J. Weston and Kiriaki Platanioti and David J. Hand},
journal= {arXiv preprint arXiv:1011.1788},
year = {2010}
}
Comments
Published in at http://dx.doi.org/10.1214/10-AOAS329 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org)